1. Introduction to COVID-19 World Vaccine Adverse Reactions Dataset
2. Project work flow
3. Challenges, solutions and tools used - Load, Clean and Augment
4. Data analysis
5. Conclusion and discussion

• Found on Kaggle (https://www.kaggle.com/ayushggarg/covid19-vaccine-adverse-reactions?select=2021VAERSSYMPTOMS.csv)
• Data from the Vaccine Adverse Event Reporting System (VAERS) created by the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC)
• Contains 3 data sets:
  1. PATIENTS.CSV
  2. VACCINES.CSV
  3. SYMPTOMS.CSV
• Data sets connected by individual IDs (VAERS_ID)
• VAERS User Guide (https://vaers.hhs.gov/docs/VAERSDataUseGuide_November2020.pdf)
  • A guide to variable names

PATIENTS.CSV: Contains information about the individuals that received the vaccines

## # A tibble: 3 x 35
##   VAERS_ID RECVDATE  STATE AGE_YRS CAGE_YR CAGE_MO SEX   RPT_DATE   SYMPTOM_TEXT
##   <chr>    <chr>     <chr>   <dbl>   <dbl>   <dbl> <chr> <date>     <chr>       
## 1 0916600  01/01/20… TX         33      33      NA F     NA         "Right side…
## 2 0916601  01/01/20… CA         73      73      NA F     NA         "Approximat…
## 3 0916602  01/01/20… WA         23      23      NA F     NA         "About 15 m…
## # … with 26 more variables: DIED <chr>, DATEDIED <chr>, L_THREAT <chr>,
## #   ER_VISIT <chr>, HOSPITAL <chr>, HOSPDAYS <dbl>, X_STAY <chr>,
## #   DISABLE <chr>, RECOVD <chr>, VAX_DATE <chr>, ONSET_DATE <chr>,
## #   NUMDAYS <dbl>, LAB_DATA <chr>, V_ADMINBY <chr>, V_FUNDBY <chr>,
## #   OTHER_MEDS <chr>, CUR_ILL <chr>, HISTORY <chr>, PRIOR_VAX <chr>,
## #   SPLTTYPE <chr>, FORM_VERS <dbl>, TODAYS_DATE <chr>, BIRTH_DEFECT <chr>,
## #   OFC_VISIT <chr>, ER_ED_VISIT <chr>, ALLERGIES <chr>

VACCINES.CSV: Contains information about the received vaccine

## # A tibble: 3 x 8
##   VAERS_ID VAX_TYPE VAX_MANU VAX_LOT VAX_DOSE_SERIES VAX_ROUTE VAX_SITE VAX_NAME
##   <chr>    <chr>    <chr>    <chr>   <chr>           <chr>     <chr>    <chr>   
## 1 0916600  COVID19  "MODERN… 037K20A 1               IM        LA       COVID19…
## 2 0916601  COVID19  "MODERN… 025L20A 1               IM        RA       COVID19…
## 3 0916602  COVID19  "PFIZER… EL1284  1               IM        LA       COVID19…

SYMPTOMS.CSV: Contains information about the symptoms experiences after vaccination

## # A tibble: 3 x 11
##   VAERS_ID SYMPTOM1      SYMPTOMVERSION1 SYMPTOM2   SYMPTOMVERSION2 SYMPTOM3    
##   <chr>    <chr>                   <dbl> <chr>                <dbl> <chr>       
## 1 0916600  Dysphagia                23.1 Epiglotti…            23.1 <NA>        
## 2 0916601  Anxiety                  23.1 Dyspnoea              23.1 <NA>        
## 3 0916602  Chest discom…            23.1 Dysphagia             23.1 Pain in ext…
## # … with 5 more variables: SYMPTOMVERSION3 <dbl>, SYMPTOM4 <chr>,
## #   SYMPTOMVERSION4 <dbl>, SYMPTOM5 <chr>, SYMPTOMVERSION5 <dbl>

Important verbs and tools used to solve challenges:

• readr: read_csv(), write_csv()
• always saving files gzipped
• dealing with string NAs (include here?)
• specifying column types (include here?)

CHALLENGE: Multiple large files

SOLUTION: Keep them compressed and only decompress when reading into R:

# Read
patients <- read_csv(file = gzfile("../data/01_patients.csv.gz"))

# Clean
patients_clean <- patients %>% ...

# Write
write_csv(x = patients, file = "../data/02_patients_clean.csv.gz")

CHALLENGE: Wrong column types automatically assigned by R

patients <- read_csv(file = gzfile("../data/01_patients.csv.gz"))

## Warning: 241 parsing failures.
##  row          col           expected     actual         file
## 1465 BIRTH_DEFECT 1/0/T/F/TRUE/FALSE Y          <connection>
## 2742 X_STAY       1/0/T/F/TRUE/FALSE Y          <connection>
## 2807 RPT_DATE     1/0/T/F/TRUE/FALSE 2021-01-04 <connection>
## 2807 V_FUNDBY     1/0/T/F/TRUE/FALSE OTH        <connection>
## 2811 RPT_DATE     1/0/T/F/TRUE/FALSE 2021-01-04 <connection>
## .... ............ .................. .......... ............
## See problems(...) for more details.

SOLUTION: Manually assign column types

patients <- read_csv(file = gzfile("../data/01_patients.csv.gz"), 
                     col_types = cols("BIRTH_DEFECT" = col_character(),
                                      "X_STAY" = col_character(),
                                      "RPT_DATE" = col_date(format="%Y-%m-%d"),
                                      "V_FUNDBY" = col_character(),
                                      "ER_VISIT" = col_character()))

CHALLENGE: NA strings (“NA”, “N/A”, “Unknown”, " "…)

SOLUTION:

patients <- read_csv(file = gzfile("../data/01_patients.csv.gz"),
                     na = c("", " ", 
                                "NA", "N/A", "na", "Na", "n/a", "N/a", 
                                "None", "none", "None.", "NONE",
                                "unknown", "Unknown", "UNKNOWN", "U",
                                "NO KNOWN", "no known", "No known", "No Known", 
                                "None known", "none known", "NONE KNOWN", "None Known", 
                                "None reported", "Not applicable",
                                "No", "NO", "no"))

Important verbs and tools used to solve challenges:

• dplyr: filter(), select(), distinct(), mutate()
• tidyr: replace_na()

I am aware of how horrible this table is :/

Important verbs and tools used to solve challenges:

• dplyr: filter(), select(), mutate(), case_when(), arrange(), group_by(), count(), distinct(), summarise(), drop_na(), rename()
• tidyr: pivot_longer(), pivot_wider(), inner_join(), full_join(), pluck()
• stringr: regular expressions, str_c(), str_replace(), str_replace()
• Anything else?

CHALLENGE: Some columns contain long string descriptions that need to be turned into something tidy

SOLUTION: Make categorical variable

Example: ALLERGIES column:

Make categorical variable that states if patient has allergies or not:

patients %>%
  mutate(HAS_ALLERGIES = case_when(grepl("^no.?$ | ^no | ^none | ^not | ^non", 
                                         ALLERGIES, 
                                         ignore.case = TRUE) ~ "N",
                                 is.na(ALLERGIES) ~ "N",
                                 TRUE ~ "Y"))

Clean categorical HAS_ALLERGIES column:

## # A tibble: 5 x 3
##   VAERS_ID ALLERGIES                                               HAS_ALLERGIES
##   <chr>    <chr>                                                   <chr>        
## 1 0916603  Diclofenac, novacaine, lidocaine, pickles, tomatoes, m… Y            
## 2 0916604  <NA>                                                    N            
## 3 0916660  Penicillin                                              Y            
## 4 0916685  none that I am aware of                                 N            
## 5 0917437  No known allergies                                      N

Another example: OTHER_MEDS column

Detect individuals that have taken anti-inflammatory or steroid drugs before vaccine (not recommended):

patients %>%
  mutate(TAKES_ANTIINFLAMATORY = 
           case_when(grepl("ibuprofen | aspirin | celecoxib | diclofenac | diflunisal | etodolac | indomethacin", 
                           OTHER_MEDS, 
                           ignore.case = TRUE) ~ "Y",
                     TRUE ~ "N")) %>% 
  mutate(TAKES_STEROIDS = 
           case_when(grepl("steroid | betamethasone | prednisolone | dexamethasone | hydrocortisone", 
                           OTHER_MEDS, 
                           ignore.case = TRUE) ~ "Y",
                     TRUE ~ "N"))

Clean, categorial TAKES_ANTIINFLAMMATORY and TAKES_STEROID columns:

## # A tibble: 4 x 4
##   VAERS_ID OTHER_MEDS                           TAKES_ANTIINFLAM… TAKES_STEROIDS
##   <chr>    <chr>                                <chr>             <chr>         
## 1 0918421  1 aspirin a day 81 mg, levothyroxin… Y                 N             
## 2 0921732  Ibuprofen - PRN  States she does no… Y                 N             
## 3 0932980  Hydrocortisone 25mg daily.  Fludroc… N                 Y             
## 4 0934539  Singulair, Oxybutynin, Fosamax, Pre… N                 Y

CHALLENGE: Symptoms are recorded in a way that makes later analysis difficult

## # A tibble: 5 x 6
##   VAERS_ID SYMPTOM1           SYMPTOM2        SYMPTOM3 SYMPTOM4         SYMPTOM5
##   <chr>    <chr>              <chr>           <chr>    <chr>            <chr>   
## 1 0916618  Injection site pa… Pain            <NA>     <NA>             <NA>    
## 2 0916619  Injection site pa… Menorrhagia     <NA>     <NA>             <NA>    
## 3 0916620  Arthralgia         Chills          Headache Mobility decrea… Myalgia 
## 4 0916620  Nausea             Pain in extrem… Pyrexia  <NA>             <NA>    
## 5 0916621  Chills             Fatigue         Headache Myalgia          <NA>

SOLUTION: 20 most common symptoms are found and turned into TRUE/FALSE columns

## # A tibble: 3 x 21
##   VAERS_ID HEADACHE PYREXIA CHILLS FATIGUE PAIN  PAIN_IN_EXTREMITY NAUSEA
##   <chr>    <lgl>    <lgl>   <lgl>  <lgl>   <lgl> <lgl>             <lgl> 
## 1 0916600  FALSE    FALSE   FALSE  FALSE   FALSE FALSE             FALSE 
## 2 0916601  FALSE    FALSE   FALSE  FALSE   FALSE FALSE             FALSE 
## 3 0916602  FALSE    FALSE   FALSE  FALSE   FALSE TRUE              FALSE 
## # … with 13 more variables: DIZZINESS <lgl>, MYALGIA <lgl>,
## #   INJECTION_SITE_ERYTHEMA <lgl>, INJECTION_SITE_PRURITUS <lgl>,
## #   INJECTION_SITE_SWELLING <lgl>, INJECTION_SITE_PAIN <lgl>, ARTHRALGIA <lgl>,
## #   DYSPNOEA <lgl>, VOMITING <lgl>, PRURITUS <lgl>, DEATH <lgl>, RASH <lgl>,
## #   ASTHENIA <lgl>

Important verbs and tools used:

• ggplot()
• geom_bar(), geom_boxplot(), geom_tile(), geom_segment()
• scales()
• fct_reorder()
• patchwork package
• viridis package

Important verbs and tools used:

• glm()
• broom: tidy(), glance()

merged_data_wide %>%
  glm(formula = DIED ~ SEX + AGE_YRS + HAS_ALLERGIES + HAS_ILLNESS + HAS_COVID + HAD_COVID, 
      family = binomial) %>%
  tidy() %>%
  mutate(odds_ratio = exp(estimate))

## # A tibble: 7 x 6
##   term           estimate std.error statistic  p.value odds_ratio
##   <chr>             <dbl>     <dbl>     <dbl>    <dbl>      <dbl>
## 1 (Intercept)    -9.34      0.161    -58.0    0         0.0000876
## 2 SEXM            0.924     0.0573    16.1    2.18e-58  2.52     
## 3 AGE_YRS         0.0915    0.00207   44.2    0         1.10     
## 4 HAS_ALLERGIESY -0.100     0.0608    -1.65   9.82e- 2  0.904    
## 5 HAS_ILLNESSY    1.10      0.0664    16.6    6.60e-62  3.01     
## 6 HAS_COVIDY     -0.117     0.148     -0.791  4.29e- 1  0.890    
## 7 HAD_COVIDY      0.00915   0.193      0.0474 9.62e- 1  1.01

merged_data_wide %>%
  glm(formula = str_c("DEATH ~ ", str_c(symptoms, collapse = "+")), 
      family = binomial) %>%
  tidy() %>%
  mutate(odds_ratio = exp(estimate))

## # A tibble: 20 x 6
##   term          estimate std.error statistic  p.value odds_ratio
##   <chr>            <dbl>     <dbl>     <dbl>    <dbl>      <dbl>
## 1 (Intercept)     -2.01     0.0287    -70.1  0             0.134
## 2 HEADACHETRUE    -1.67     0.156     -10.7  7.92e-27      0.188
## 3 PYREXIATRUE     -0.429    0.112      -3.82 1.34e- 4      0.651
## 4 CHILLSTRUE      -1.21     0.171      -7.11 1.17e-12      0.298
## 5 FATIGUETRUE     -0.367    0.115      -3.19 1.41e- 3      0.693
## 6 PAINTRUE        -0.913    0.153      -5.98 2.17e- 9      0.401
## 7 NAUSEATRUE      -0.621    0.139      -4.46 8.17e- 6      0.538
## 8 DIZZINESSTRUE   -2.17     0.193     -11.2  2.87e-29      0.114
## # … with 12 more rows

merged_data_long %>%
  select(TAKES_ANTIINFLAMATORY, SYMPTOM, SYMPTOM_VALUE) %>%
  group_by(SYMPTOM) %>%
  nest %>% 
  ungroup %>%
  mutate(mdl = map(data, ~glm(TAKES_ANTIINFLAMATORY ~ SYMPTOM_VALUE,
                              family = binomial))) %>%
  mutate(mdl_tidy = map(mdl, ~tidy(.x, conf.int = TRUE)))

## # A tibble: 20 x 9
##   SYMPTOM  estimate std.error statistic p.value conf.low conf.high odds_ratio
##   <chr>       <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>      <dbl>
## 1 HEADACHE  -0.170     0.0954    -1.79   0.0742   -0.361    0.0133      0.843
## 2 PYREXIA    0.0734    0.0967     0.760  0.448    -0.120    0.259       1.08 
## 3 CHILLS    -0.0727    0.103     -0.703  0.482    -0.280    0.126       0.930
## 4 FATIGUE    0.0226    0.102      0.221  0.825    -0.183    0.219       1.02 
## 5 PAIN       0.0190    0.106      0.179  0.858    -0.194    0.222       1.02 
## # … with 15 more rows, and 1 more variable: identified_as <chr>

Important verbs and tools used:

• prop_test()
• chisq.test()

Important verbs and tools used:

• prcomp()
• kmeans()
• tidymodels: (used for what?)